Quick check of the first no-genetic data demultiplex sequencing from the pilot. NPCs
library(Seurat)
The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed.
library(tidyverse)
Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
── Attaching packages ──────────────────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.2 ✔ purrr 1.0.1
✔ tibble 3.2.1 ✔ dplyr 1.1.2
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ─────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
#library(DoubletFinder)
library(enrichR)
Welcome to enrichR
Checking connection ...
Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!
OxEnrichr ... Connection is available!
library(clustree)
Loading required package: ggraph
#library("scClassify")
#library(SingleCellExperiment)
#library("Matrix")
Read in the data
seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/seu_souporcell_ADHD.rds")
colnames(seu@meta.data) # shows you the meta data
unique(seu$orig.ident)
unique(seu$souporcell_assignment)
unique(seu$souporcell_status)
dim(seu)
table(seu$souporcell_assignment)
Look at the sequencing
VlnPlot(seu, features = c("nCount_RNA","nFeature_RNA"), pt.size = 0.001)
VlnPlot(seu, features = c("nFeature_RNA"), pt.size = 0.001, y.max = 500)
VlnPlot(seu, features = c("nCount_RNA"), pt.size = 0.001, y.max = 50000)
VlnPlot(seu, features = c("nCount_RNA"), pt.size = 0.001, y.max = 5000)
seu$nFeature_RNA %>% summary
seu$nCount_RNA %>% summary
table(seu$souporcell_status)
dim(seu)
Calculate the percent mitochondrial genes
seu <- PercentageFeatureSet(seu, pattern = "^MT-", col.name = "percent.MT")
seu$percent.MT %>% summary
VlnPlot(seu, features = "percent.MT", pt.size = 0.001)
VlnPlot(seu, features = "percent.MT", pt.size = 0.001, y.max = 20)
I’ll use 10% as a cutoff and check if this removes more of the low read cells. I believe this object is already the CellRanger Filtered object.
#Remove any cells with more than 10% mitochondrial counts
#seu.ft <- subset(seu, percent.MT < 10)
#dim(seu)
#dim(seu.ft)
# removed 656 cells
seu.ft <- subset(seu, percent.MT < 20)
dim(seu)
dim(seu.ft)
# removed 339 cells
VlnPlot(seu.ft, features = c("nCount_RNA","nFeature_RNA","percent.MT"), pt.size = 0.001)
VlnPlot(seu, features = c("nCount_RNA"), group.by = "souporcell_status", y.max = 6000)
Warning: Removed 14234 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 14234 rows containing missing values (`geom_point()`).
VlnPlot(seu, features = c("nFeature_RNA"), group.by = "souporcell_status", y.max = 3000)
Warning: Removed 9246 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 9246 rows containing missing values (`geom_point()`).
I will filter for higher counts and lower counts
# we aren't adding any more filters here
clean up
seu <- seu.ft
rm(seu.ft)
Remove the doublets and the unassigned cells
dim(seu)
[1] 33538 14643
Now the workflow to make clusters
seu <- NormalizeData(seu, normalization.method = "LogNormalize", scale.factor = 10000)
Performing log-normalization
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seu <- FindVariableFeatures(seu, selection.method = "vst", nfeatures = 2500)
Calculating gene variances
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**************************************************|
Calculating feature variances of standardized and clipped values
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var <- VariableFeatures(seu)
top10 <- var[1:10]
p1 <- VariableFeaturePlot(seu)
p2 <- LabelPoints(plot = p1, points = top10, repel = TRUE)
When using repel, set xnudge and ynudge to 0 for optimal results
p2
Warning: Transformation introduced infinite values in continuous x-axis
Scale and get PCA
#Linear dimensionality reduction
#Choosing the number of PCs can depend on how many cells you have
seu <- ScaleData(seu)
Centering and scaling data matrix
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seu <- RunPCA(seu, assay = "RNA", npcs = 30)
PC_ 1
Positive: CKB, TUBB2B, CRABP1, TUBA1A, MAP1B, TTYH1, SOX2, POU3F2, BEX1, CRMP1
HMGA1, KIF5C, MAP2, RFX4, HES6, SOX11, DRAXIN, FGFBP3, PRTG, SRGAP3
HSP90AA1, TAGLN3, MIAT, LIN28A, GPM6B, SYT11, MAP6, ELAVL2, DCX, ELAVL3
Negative: COL3A1, S100A11, LGALS1, LUM, DCN, SPARC, COL1A1, PCOLCE, B2M, CDH11
SERPINH1, APOE, COL5A2, FN1, COL5A1, COL6A2, IFITM3, RRBP1, TIMP1, SELENOM
COLEC12, BGN, COL6A3, LAPTM4A, CALD1, COL1A2, EMP3, IFI16, TPM1, CCDC80
PC_ 2
Positive: HMGB2, CENPF, TOP2A, CKS1B, MKI67, NUSAP1, BIRC5, TPX2, HMGB1, CCNB1
H2AFZ, ASPM, TUBA1B, H2AFX, UBE2C, CKS2, SMC4, MAD2L1, DLGAP5, GTSE1
CENPE, PTTG1, TYMS, TUBB4B, PCLAF, CDK1, CCNA2, CDC20, KIF20B, KIF11
Negative: STMN2, DCX, TAGLN3, ELAVL3, INA, STMN4, NEFM, KLHL35, ELAVL4, NHLH1
ONECUT2, SCG3, DCC, SRRM4, SYT1, INSM1, DLL3, CNTN2, MLLT11, CDKN1C
NCAM1, TFAP2B, CLDN5, ATCAY, SSTR2, SOX4, GDAP1L1, GNG3, ELAVL2, PCBP4
PC_ 3
Positive: IFI44L, PRRX1, SMOC2, PTH1R, COLEC12, SFRP1, LRRC17, CDC42EP5, DAB2, TWIST1
SLC1A3, TPM1, ZIC1, CPE, NAV3, FOXC1, IFI44, FRZB, COL26A1, ID1
DACT2, PRRX2, LAMA4, FN1, TCIM, PMP22, EDNRA, ISG15, AKAP12, NFIA
Negative: KRT19, KRT8, THY1, TM4SF1, NPNT, S100A10, KRT18, HAND2, MYRF, NNMT
DSG2, MATN2, GATA6, FXYD5, RPP25, ASPHD1, PLP2, SLC9A3R1, KCNK6, LRRN4
PITX1, MAN1A1, CYBA, LMCD1, KDR, EZR, TAGLN2, COL1A1, BNC1, ANXA1
PC_ 4
Positive: NEB, DES, KLHL41, TNNI1, TNNT1, TTN, CHRNA1, MYL1, MYL4, MYOG
TNNC2, ENO3, CDH15, MYOD1, ACTC1, TNNT2, MYLPF, SMPX, SGCA, AC020909.2
SYNPO2L, IL17B, MRLN, FITM1, MYH3, ATP2A1, SMYD1, TRIM55, TNNC1, RAPSN
Negative: PTN, IGFBP2, CRABP1, IFI16, GJA1, TTYH1, PDLIM1, ZIC2, COL11A1, CLU
SOX2, FABP5, IFIT3, MGP, PARP14, SFRP2, IFITM1, SERPING1, VCAN, SP100
HLA-C, SFRP1, C7, B2M, DAB2, ARHGAP29, IFI6, HLA-E, GPM6B, LIFR
PC_ 5
Positive: RPL12, VIM, KRT19, MYLPF, IGFBP5, MYL1, LMCD1, TNNC2, AL359091.1, MYL4
HAND2, HES5, TNNC1, NPNT, NPPC, FXYD5, PITX1, NUPR1, KRT8, ACTC1
TNNI1, TNNT2, MYRF, SPRR2F, AL139246.5, IL17B, TTYH1, HSPB7, REC8, RPP25
Negative: ASPM, ARL6IP1, CENPE, NEFM, TPX2, UBE2S, GAP43, STMN2, TOP2A, MKI67
KPNA2, NCAM1, INA, GTSE1, SGO2, UBE2C, STMN4, ELAVL3, MAP1B, DLGAP5
NEFL, NUSAP1, PLK1, CENPF, ONECUT2, CCNB1, DCX, ACTB, KIF20B, HMMR
PCAPlot(seu)
#Assess how many PCs capture most of the information in the data
ElbowPlot(seu, ndims = 30)
20 PCs are good now make the UMAP
seu <- RunUMAP(seu, dims = 1:20, n.neighbors = 121)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
15:34:51 UMAP embedding parameters a = 0.9922 b = 1.112
15:34:51 Read 14643 rows and found 20 numeric columns
15:34:51 Using Annoy for neighbor search, n_neighbors = 121
15:34:51 Building Annoy index with metric = cosine, n_trees = 50
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15:34:53 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//Rtmpo5qc3e/file10214368d353
15:34:53 Searching Annoy index using 1 thread, search_k = 12100
15:35:09 Annoy recall = 100%
15:35:09 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 121
15:35:13 Initializing from normalized Laplacian + noise (using irlba)
15:35:14 Commencing optimization for 200 epochs, with 1862028 positive edges
Using method 'umap'
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15:35:36 Optimization finished
DimPlot(seu, group.by = "orig.ident")
Find clusters
seu <- FindNeighbors(seu, dims = 1:20, k.param = 121)
Computing nearest neighbor graph
Computing SNN
# the number of clusters is dependent on the resolution a number from 0-2.
# Higher values make more clusters
# we include
seu <- FindClusters(seu, resolution = c(0,0.05,0.25,0.6,1,1.5) )
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2834426
Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 1.0000
Number of communities: 1
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2834426
Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9746
Number of communities: 5
Elapsed time: 12 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2834426
Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9249
Number of communities: 7
Elapsed time: 9 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2834426
Running Louvain algorithm...
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8785
Number of communities: 10
Elapsed time: 13 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2834426
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8384
Number of communities: 13
Elapsed time: 11 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2834426
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8012
Number of communities: 20
Elapsed time: 9 seconds
# we can visualize which cells are grouped together at different resolutions using clustree
clustree(seu, prefix = "RNA_snn_res.")
# 0.6 looks good to annotate. Each cluster is splitting apart up to this point and then the cells start merging and changing clusters.
Visualize
DimPlot(seu, group.by = "RNA_snn_res.0.6", label = TRUE)
DimPlot(seu, group.by = "RNA_snn_res.1", label = TRUE)
# clustering could be improved
Look at expression profiles of known cell type markers
da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
mature_neurons = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = inh = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")
opc <-
radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")
microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
"CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
"PROS1")
features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")
short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")
View on UMAP
Idents(seu) <- "RNA_snn_res.0.6"
for (i in NPC_c) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Warning in FeaturePlot(seu, features = i, min.cutoff = "q1", max.cutoff = "q97", :
All cells have the same value (0) of FOXG1.
Idents(seu) <- "RNA_snn_res.0.6"
for (i in astrocytes) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "RNA_snn_res.0.6"
for (i in radial_glia) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Warning in FeaturePlot(seu, features = i, min.cutoff = "q1", max.cutoff = "q97", :
All cells have the same value (0) of ADGRE1.
Idents(seu) <- "RNA_snn_res.0.6"
mature_neuronsB = c("RBFOX3","SYP","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
for (i in mature_neuronsB) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
NA
NA
NA
Dot plots and heatmaps
DotPlot(seu, features = radial_glia)+ RotatedAxis()
DotPlot(seu, features = NPC_orStemLike) + RotatedAxis()
Warning in FetchData.Seurat(object = object, vars = features, cells = cells) :
The following requested variables were not found: MASH1
DotPlot(seu, features = astrocytes) + RotatedAxis()
DotPlot(seu, features = NPC_c) + RotatedAxis()
Find cluster markers
Idents(seu) <- "RNA_snn_res.0.6"
ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)
Calculating cluster 0
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Calculating cluster 1
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Calculating cluster 2
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|++ | 2 % ~15s
|++ | 4 % ~15s
|+++ | 5 % ~14s
|++++ | 6 % ~14s
|++++ | 7 % ~14s
|+++++ | 9 % ~14s
|+++++ | 10% ~13s
|++++++ | 11% ~13s
|+++++++ | 12% ~13s
|+++++++ | 13% ~13s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 18% ~12s
|++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|+++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 24% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~10s
|+++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|++++++++++++++++ | 30% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 37% ~09s
|+++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|++++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~07s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|+++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|++++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~16s
|+ | 2 % ~17s
|++ | 3 % ~16s
|++ | 4 % ~16s
|+++ | 5 % ~15s
|+++ | 6 % ~15s
|++++ | 7 % ~15s
|++++ | 8 % ~14s
|+++++ | 9 % ~14s
|+++++ | 10% ~14s
|++++++ | 11% ~14s
|++++++ | 12% ~14s
|+++++++ | 13% ~13s
|+++++++ | 14% ~13s
|++++++++ | 15% ~13s
|++++++++ | 16% ~13s
|+++++++++ | 17% ~13s
|+++++++++ | 18% ~13s
|++++++++++ | 19% ~13s
|++++++++++ | 20% ~12s
|+++++++++++ | 21% ~12s
|+++++++++++ | 22% ~12s
|++++++++++++ | 23% ~12s
|++++++++++++ | 24% ~12s
|+++++++++++++ | 25% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~11s
|++++++++++++++ | 28% ~11s
|+++++++++++++++ | 29% ~11s
|+++++++++++++++ | 30% ~11s
|++++++++++++++++ | 31% ~11s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~10s
|+++++++++++++++++ | 34% ~10s
|++++++++++++++++++ | 35% ~10s
|++++++++++++++++++ | 36% ~10s
|+++++++++++++++++++ | 37% ~10s
|+++++++++++++++++++ | 38% ~10s
|++++++++++++++++++++ | 39% ~10s
|++++++++++++++++++++ | 40% ~09s
|+++++++++++++++++++++ | 41% ~09s
|+++++++++++++++++++++ | 42% ~09s
|++++++++++++++++++++++ | 43% ~09s
|++++++++++++++++++++++ | 44% ~09s
|+++++++++++++++++++++++ | 45% ~09s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~08s
|++++++++++++++++++++++++ | 48% ~08s
|+++++++++++++++++++++++++ | 49% ~08s
|+++++++++++++++++++++++++ | 50% ~08s
|++++++++++++++++++++++++++ | 51% ~08s
|++++++++++++++++++++++++++ | 52% ~08s
|+++++++++++++++++++++++++++ | 53% ~07s
|+++++++++++++++++++++++++++ | 54% ~07s
|++++++++++++++++++++++++++++ | 55% ~07s
|++++++++++++++++++++++++++++ | 56% ~07s
|+++++++++++++++++++++++++++++ | 57% ~07s
|+++++++++++++++++++++++++++++ | 58% ~07s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~06s
|++++++++++++++++++++++++++++++++ | 63% ~06s
|++++++++++++++++++++++++++++++++ | 64% ~06s
|+++++++++++++++++++++++++++++++++ | 65% ~06s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|++++++++++++++++++++++++++++++++++ | 68% ~05s
|+++++++++++++++++++++++++++++++++++ | 69% ~05s
|+++++++++++++++++++++++++++++++++++ | 70% ~05s
|++++++++++++++++++++++++++++++++++++ | 71% ~05s
|++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|+++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 75% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~04s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=16s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~27s
|++ | 2 % ~27s
|++ | 3 % ~27s
|+++ | 4 % ~26s
|+++ | 5 % ~25s
|++++ | 6 % ~25s
|++++ | 7 % ~25s
|+++++ | 8 % ~24s
|+++++ | 9 % ~24s
|++++++ | 10% ~24s
|++++++ | 11% ~23s
|+++++++ | 12% ~23s
|+++++++ | 14% ~23s
|++++++++ | 15% ~22s
|++++++++ | 16% ~22s
|+++++++++ | 17% ~22s
|+++++++++ | 18% ~21s
|++++++++++ | 19% ~21s
|++++++++++ | 20% ~21s
|+++++++++++ | 21% ~21s
|+++++++++++ | 22% ~20s
|++++++++++++ | 23% ~20s
|++++++++++++ | 24% ~20s
|+++++++++++++ | 25% ~20s
|++++++++++++++ | 26% ~19s
|++++++++++++++ | 27% ~19s
|+++++++++++++++ | 28% ~19s
|+++++++++++++++ | 29% ~18s
|++++++++++++++++ | 30% ~18s
|++++++++++++++++ | 31% ~18s
|+++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 33% ~17s
|++++++++++++++++++ | 34% ~17s
|++++++++++++++++++ | 35% ~17s
|+++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 38% ~16s
|++++++++++++++++++++ | 39% ~16s
|++++++++++++++++++++ | 40% ~16s
|+++++++++++++++++++++ | 41% ~15s
|+++++++++++++++++++++ | 42% ~15s
|++++++++++++++++++++++ | 43% ~15s
|++++++++++++++++++++++ | 44% ~15s
|+++++++++++++++++++++++ | 45% ~14s
|+++++++++++++++++++++++ | 46% ~14s
|++++++++++++++++++++++++ | 47% ~14s
|++++++++++++++++++++++++ | 48% ~13s
|+++++++++++++++++++++++++ | 49% ~13s
|+++++++++++++++++++++++++ | 50% ~13s
|++++++++++++++++++++++++++ | 51% ~13s
|+++++++++++++++++++++++++++ | 52% ~12s
|+++++++++++++++++++++++++++ | 53% ~12s
|++++++++++++++++++++++++++++ | 54% ~12s
|++++++++++++++++++++++++++++ | 55% ~12s
|+++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 57% ~11s
|++++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~10s
|+++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~10s
|++++++++++++++++++++++++++++++++ | 62% ~10s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|+++++++++++++++++++++++++++++++++ | 66% ~09s
|++++++++++++++++++++++++++++++++++ | 67% ~09s
|++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|+++++++++++++++++++++++++++++++++++ | 70% ~08s
|++++++++++++++++++++++++++++++++++++ | 71% ~08s
|++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=26s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~01m 06s
|++ | 2 % ~01m 08s
|++ | 3 % ~01m 14s
|+++ | 4 % ~01m 17s
|+++ | 5 % ~01m 18s
|++++ | 6 % ~01m 18s
|++++ | 7 % ~01m 18s
|+++++ | 8 % ~01m 17s
|+++++ | 9 % ~01m 16s
|++++++ | 10% ~01m 15s
|++++++ | 11% ~01m 12s
|+++++++ | 12% ~01m 12s
|+++++++ | 13% ~01m 12s
|++++++++ | 14% ~01m 09s
|++++++++ | 15% ~01m 08s
|+++++++++ | 16% ~01m 06s
|+++++++++ | 18% ~01m 04s
|++++++++++ | 19% ~01m 03s
|++++++++++ | 20% ~01m 02s
|+++++++++++ | 21% ~01m 00s
|+++++++++++ | 22% ~59s
|++++++++++++ | 23% ~57s
|++++++++++++ | 24% ~57s
|+++++++++++++ | 25% ~56s
|+++++++++++++ | 26% ~55s
|++++++++++++++ | 27% ~54s
|++++++++++++++ | 28% ~53s
|+++++++++++++++ | 29% ~52s
|+++++++++++++++ | 30% ~51s
|++++++++++++++++ | 31% ~50s
|++++++++++++++++ | 32% ~49s
|+++++++++++++++++ | 33% ~48s
|++++++++++++++++++ | 34% ~47s
|++++++++++++++++++ | 35% ~46s
|+++++++++++++++++++ | 36% ~45s
|+++++++++++++++++++ | 37% ~44s
|++++++++++++++++++++ | 38% ~44s
|++++++++++++++++++++ | 39% ~43s
|+++++++++++++++++++++ | 40% ~42s
|+++++++++++++++++++++ | 41% ~41s
|++++++++++++++++++++++ | 42% ~40s
|++++++++++++++++++++++ | 43% ~39s
|+++++++++++++++++++++++ | 44% ~38s
|+++++++++++++++++++++++ | 45% ~38s
|++++++++++++++++++++++++ | 46% ~37s
|++++++++++++++++++++++++ | 47% ~36s
|+++++++++++++++++++++++++ | 48% ~35s
|+++++++++++++++++++++++++ | 49% ~34s
|++++++++++++++++++++++++++ | 51% ~34s
|++++++++++++++++++++++++++ | 52% ~33s
|+++++++++++++++++++++++++++ | 53% ~32s
|+++++++++++++++++++++++++++ | 54% ~31s
|++++++++++++++++++++++++++++ | 55% ~30s
|++++++++++++++++++++++++++++ | 56% ~30s
|+++++++++++++++++++++++++++++ | 57% ~29s
|+++++++++++++++++++++++++++++ | 58% ~28s
|++++++++++++++++++++++++++++++ | 59% ~27s
|++++++++++++++++++++++++++++++ | 60% ~27s
|+++++++++++++++++++++++++++++++ | 61% ~26s
|+++++++++++++++++++++++++++++++ | 62% ~25s
|++++++++++++++++++++++++++++++++ | 63% ~25s
|++++++++++++++++++++++++++++++++ | 64% ~24s
|+++++++++++++++++++++++++++++++++ | 65% ~23s
|+++++++++++++++++++++++++++++++++ | 66% ~22s
|++++++++++++++++++++++++++++++++++ | 67% ~22s
|+++++++++++++++++++++++++++++++++++ | 68% ~21s
|+++++++++++++++++++++++++++++++++++ | 69% ~20s
|++++++++++++++++++++++++++++++++++++ | 70% ~20s
|++++++++++++++++++++++++++++++++++++ | 71% ~19s
|+++++++++++++++++++++++++++++++++++++ | 72% ~18s
|+++++++++++++++++++++++++++++++++++++ | 73% ~18s
|++++++++++++++++++++++++++++++++++++++ | 74% ~17s
|++++++++++++++++++++++++++++++++++++++ | 75% ~16s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~15s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~15s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~14s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~13s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~12s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~11s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~11s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~10s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~09s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 05s
Calculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~31s
|++ | 2 % ~31s
|++ | 3 % ~31s
|+++ | 4 % ~30s
|+++ | 5 % ~30s
|++++ | 6 % ~29s
|++++ | 7 % ~29s
|+++++ | 8 % ~29s
|+++++ | 9 % ~29s
|++++++ | 11% ~29s
|++++++ | 12% ~28s
|+++++++ | 13% ~28s
|+++++++ | 14% ~28s
|++++++++ | 15% ~27s
|++++++++ | 16% ~27s
|+++++++++ | 17% ~27s
|+++++++++ | 18% ~26s
|++++++++++ | 19% ~26s
|++++++++++ | 20% ~25s
|+++++++++++ | 21% ~25s
|++++++++++++ | 22% ~25s
|++++++++++++ | 23% ~24s
|+++++++++++++ | 24% ~24s
|+++++++++++++ | 25% ~24s
|++++++++++++++ | 26% ~23s
|++++++++++++++ | 27% ~23s
|+++++++++++++++ | 28% ~22s
|+++++++++++++++ | 29% ~22s
|++++++++++++++++ | 31% ~22s
|++++++++++++++++ | 32% ~21s
|+++++++++++++++++ | 33% ~21s
|+++++++++++++++++ | 34% ~21s
|++++++++++++++++++ | 35% ~21s
|++++++++++++++++++ | 36% ~20s
|+++++++++++++++++++ | 37% ~20s
|+++++++++++++++++++ | 38% ~20s
|++++++++++++++++++++ | 39% ~20s
|++++++++++++++++++++ | 40% ~19s
|+++++++++++++++++++++ | 41% ~19s
|++++++++++++++++++++++ | 42% ~19s
|++++++++++++++++++++++ | 43% ~18s
|+++++++++++++++++++++++ | 44% ~18s
|+++++++++++++++++++++++ | 45% ~18s
|++++++++++++++++++++++++ | 46% ~18s
|++++++++++++++++++++++++ | 47% ~17s
|+++++++++++++++++++++++++ | 48% ~17s
|+++++++++++++++++++++++++ | 49% ~16s
|++++++++++++++++++++++++++ | 51% ~16s
|++++++++++++++++++++++++++ | 52% ~16s
|+++++++++++++++++++++++++++ | 53% ~15s
|+++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~15s
|++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|+++++++++++++++++++++++++++++ | 58% ~14s
|++++++++++++++++++++++++++++++ | 59% ~13s
|++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 61% ~13s
|++++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~12s
|+++++++++++++++++++++++++++++++++ | 64% ~12s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|++++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~11s
|+++++++++++++++++++++++++++++++++++ | 68% ~10s
|+++++++++++++++++++++++++++++++++++ | 69% ~10s
|++++++++++++++++++++++++++++++++++++ | 71% ~10s
|++++++++++++++++++++++++++++++++++++ | 72% ~09s
|+++++++++++++++++++++++++++++++++++++ | 73% ~09s
|+++++++++++++++++++++++++++++++++++++ | 74% ~09s
|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~07s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=32s
Calculating cluster 7
| | 0 % ~calculating
|+ | 2 % ~06s
|++ | 3 % ~06s
|+++ | 5 % ~06s
|++++ | 6 % ~06s
|+++++ | 8 % ~06s
|+++++ | 10% ~06s
|++++++ | 11% ~06s
|+++++++ | 13% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 16% ~06s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~05s
|+++++++++++ | 21% ~05s
|++++++++++++ | 23% ~05s
|+++++++++++++ | 24% ~05s
|+++++++++++++ | 26% ~05s
|++++++++++++++ | 27% ~05s
|+++++++++++++++ | 29% ~05s
|++++++++++++++++ | 31% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 37% ~04s
|++++++++++++++++++++ | 39% ~04s
|+++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 44% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 48% ~04s
|+++++++++++++++++++++++++ | 50% ~04s
|++++++++++++++++++++++++++ | 52% ~04s
|+++++++++++++++++++++++++++ | 53% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|++++++++++++++++++++++++++++++ | 60% ~03s
|+++++++++++++++++++++++++++++++ | 61% ~03s
|++++++++++++++++++++++++++++++++ | 63% ~03s
|+++++++++++++++++++++++++++++++++ | 65% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 73% ~02s
|++++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 76% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~23s
|++ | 2 % ~25s
|++ | 3 % ~26s
|+++ | 4 % ~25s
|+++ | 5 % ~25s
|++++ | 6 % ~24s
|++++ | 7 % ~23s
|+++++ | 9 % ~23s
|+++++ | 10% ~22s
|++++++ | 11% ~22s
|++++++ | 12% ~22s
|+++++++ | 13% ~21s
|+++++++ | 14% ~21s
|++++++++ | 15% ~20s
|++++++++ | 16% ~20s
|+++++++++ | 17% ~20s
|++++++++++ | 18% ~20s
|++++++++++ | 19% ~19s
|+++++++++++ | 20% ~19s
|+++++++++++ | 21% ~19s
|++++++++++++ | 22% ~18s
|++++++++++++ | 23% ~18s
|+++++++++++++ | 24% ~18s
|+++++++++++++ | 26% ~18s
|++++++++++++++ | 27% ~18s
|++++++++++++++ | 28% ~18s
|+++++++++++++++ | 29% ~18s
|+++++++++++++++ | 30% ~17s
|++++++++++++++++ | 31% ~17s
|++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 33% ~16s
|++++++++++++++++++ | 34% ~16s
|++++++++++++++++++ | 35% ~16s
|+++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 37% ~15s
|++++++++++++++++++++ | 38% ~15s
|++++++++++++++++++++ | 39% ~15s
|+++++++++++++++++++++ | 40% ~15s
|+++++++++++++++++++++ | 41% ~14s
|++++++++++++++++++++++ | 43% ~14s
|++++++++++++++++++++++ | 44% ~14s
|+++++++++++++++++++++++ | 45% ~13s
|+++++++++++++++++++++++ | 46% ~13s
|++++++++++++++++++++++++ | 47% ~13s
|++++++++++++++++++++++++ | 48% ~13s
|+++++++++++++++++++++++++ | 49% ~12s
|+++++++++++++++++++++++++ | 50% ~12s
|++++++++++++++++++++++++++ | 51% ~12s
|+++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|++++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~11s
|+++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|++++++++++++++++++++++++++++++ | 59% ~10s
|++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|+++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~09s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|+++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|+++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|++++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=24s
Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~22s
|++ | 2 % ~21s
|++ | 3 % ~22s
|+++ | 4 % ~21s
|+++ | 6 % ~21s
|++++ | 7 % ~20s
|++++ | 8 % ~19s
|+++++ | 9 % ~19s
|+++++ | 10% ~19s
|++++++ | 11% ~18s
|+++++++ | 12% ~18s
|+++++++ | 13% ~17s
|++++++++ | 14% ~17s
|++++++++ | 16% ~17s
|+++++++++ | 17% ~17s
|+++++++++ | 18% ~17s
|++++++++++ | 19% ~17s
|++++++++++ | 20% ~17s
|+++++++++++ | 21% ~16s
|++++++++++++ | 22% ~16s
|++++++++++++ | 23% ~16s
|+++++++++++++ | 24% ~16s
|+++++++++++++ | 26% ~15s
|++++++++++++++ | 27% ~15s
|++++++++++++++ | 28% ~15s
|+++++++++++++++ | 29% ~14s
|+++++++++++++++ | 30% ~14s
|++++++++++++++++ | 31% ~14s
|+++++++++++++++++ | 32% ~14s
|+++++++++++++++++ | 33% ~13s
|++++++++++++++++++ | 34% ~13s
|++++++++++++++++++ | 36% ~13s
|+++++++++++++++++++ | 37% ~13s
|+++++++++++++++++++ | 38% ~13s
|++++++++++++++++++++ | 39% ~12s
|++++++++++++++++++++ | 40% ~12s
|+++++++++++++++++++++ | 41% ~12s
|++++++++++++++++++++++ | 42% ~12s
|++++++++++++++++++++++ | 43% ~11s
|+++++++++++++++++++++++ | 44% ~11s
|+++++++++++++++++++++++ | 46% ~11s
|++++++++++++++++++++++++ | 47% ~11s
|++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~10s
|+++++++++++++++++++++++++ | 50% ~10s
|++++++++++++++++++++++++++ | 51% ~10s
|+++++++++++++++++++++++++++ | 52% ~10s
|+++++++++++++++++++++++++++ | 53% ~09s
|++++++++++++++++++++++++++++ | 54% ~09s
|++++++++++++++++++++++++++++ | 56% ~09s
|+++++++++++++++++++++++++++++ | 57% ~09s
|+++++++++++++++++++++++++++++ | 58% ~08s
|++++++++++++++++++++++++++++++ | 59% ~08s
|++++++++++++++++++++++++++++++ | 60% ~08s
|+++++++++++++++++++++++++++++++ | 61% ~08s
|++++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~07s
|+++++++++++++++++++++++++++++++++ | 64% ~07s
|+++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|++++++++++++++++++++++++++++++++++ | 68% ~06s
|+++++++++++++++++++++++++++++++++++ | 69% ~06s
|+++++++++++++++++++++++++++++++++++ | 70% ~06s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|+++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~05s
|++++++++++++++++++++++++++++++++++++++ | 74% ~05s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~04s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=20s
Have a look at the top cluster markers
head(ClusterMarkers)
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt =avg_log2FC)
top2 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=2, wt =avg_log2FC)
DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6")
Warning in DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6") :
The following features were omitted as they were not found in the scale.data slot for the RNA assay: MTRNR2L12
DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "RNA_snn_res.0.6")
Now use EnrichR to check cell type libraries
setEnrichrSite("Enrichr") # Human genes
Connection changed to https://maayanlab.cloud/Enrichr/
Connection is Live!
# list of all the databases
# get the possible libraries
dbs <- listEnrichrDbs()
# this will list the possible libraries
dbs
# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# function for a quick look
checkCelltypes <- function(cluster_num = 0){
clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
genes <- clusterX$gene
# the cell type libraries
# get the results for each library
clusterX.cell <- enrichr(genes, databases = db)
# visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))
}
Check each cluster
cluster0 <- checkCelltypes(cluster_num = 0)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster1 <- checkCelltypes(cluster_num = 1)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster2 <- checkCelltypes(cluster_num = 2)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster3 <- checkCelltypes(cluster_num = 3)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster4 <- checkCelltypes(cluster_num = 4)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster5 <- checkCelltypes(cluster_num = 5)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster6 <- checkCelltypes(cluster_num = 6)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster7 <- checkCelltypes(cluster_num = 7)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster8 <- checkCelltypes(cluster_num = 8)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
cluster9 <- checkCelltypes(cluster_num = 9)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
Add cell type annotations
# we need to set the identity to rename
Idents(seu) <- "RNA_snn_res.0.6"
# we need to make a vector of the cell type in the same order - in the cluster order
cell_types <- c("NPC-pluri", "NPC","NPC",
"glia","glia","Neuron",
"glia-APOE","NPC-mixed","NPC",
"NPC-TNC")
names(cell_types) <- levels(seu)
seu <- RenameIdents(seu, cell_types)
seu <- AddMetaData(object=seu, metadata=Idents(seu), col.name = "CellTypes")
DimPlot(seu, label = TRUE)
Idents(seu) <- "RNA_snn_res.0.6"
cell_types <- c("NPC-pluri", "NPC1","NPC2",
"glia1","glia2","Neuron",
"glia3","NPC-mixed","NPC-div",
"NPC-TNC")
names(cell_types) <- levels(seu)
seu <- RenameIdents(seu, cell_types)
seu <- AddMetaData(object=seu, metadata=Idents(seu), col.name = "Celltypes1")
DimPlot(seu, label = TRUE)
NA
NA
See cell counts for each line and cell type
VlnPlot(seu, features = "nFeature_RNA", group.by = "souporcell_assignment")
Idents(seu)<- "CellTypes"
DimPlot(seu, group.by = "souporcell_assignment", label = TRUE)
We might need to separate and harmonize each cell type.
# make a list of seurat objects by our cell type variable
sublist <- SplitObject(seu, split.by = "souporcell_assignment")
# normalize and find variable features
for (i in 1:length(sublist)){
sublist[[i]] <- NormalizeData(sublist[[i]], verbose = FALSE)
sublist[[i]] <- FindVariableFeatures(sublist[[i]], selection.method = "vst")
}
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
# Create an empty Seurat object to store the integrated data
# Take the first Seurat object from the list as the starting point
integrated_seurat <- subset(sublist[[1]])
# Iterate over the list of Seurat objects
for (i in 1:length(sublist)) {
# Rename the 'orig.ident' metadata inside the seurat object to match the object name in the list
sublist[[i]]$orig.ident <- names(sublist)[i]
}
sample.list <- sublist
for (i in 1:length(sample.list)) {
# Normalize and scale the data
sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
# Find variable features
sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
# Get the variable features
variable_features <- VariableFeatures(sample.list[[i]])
# Run PCA with the variable features
sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
}
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = 1:30, reduction = "rpca")
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|++++ | 8 % ~08s
|++++++++ | 15% ~04s
|++++++++++++ | 23% ~03s
|++++++++++++++++ | 31% ~03s
|++++++++++++++++++++ | 38% ~02s
|++++++++++++++++++++++++ | 46% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~01s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s
Computing within dataset neighborhoods
| | 0 % ~calculating
|++++ | 8 % ~09s
|++++++++ | 15% ~06s
|++++++++++++ | 23% ~05s
|++++++++++++++++ | 31% ~05s
|++++++++++++++++++++ | 38% ~04s
|++++++++++++++++++++++++ | 46% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04s
Finding all pairwise anchors
| | 0 % ~calculating
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 913 anchors
|+ | 1 % ~04m 16s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1393 anchors
|++ | 3 % ~04m 54s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 995 anchors
|++ | 4 % ~04m 18s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 777 anchors
|+++ | 5 % ~04m 20s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 838 anchors
|++++ | 6 % ~03m 58s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1046 anchors
|++++ | 8 % ~03m 50s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 287 anchors
|+++++ | 9 % ~03m 36s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 356 anchors
|++++++ | 10% ~03m 17s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 410 anchors
|++++++ | 12% ~03m 07s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 674 anchors
|+++++++ | 13% ~02m 58s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1419 anchors
|++++++++ | 14% ~02m 56s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 939 anchors
|++++++++ | 15% ~02m 49s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1154 anchors
|+++++++++ | 17% ~02m 44s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 709 anchors
|+++++++++ | 18% ~02m 41s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 354 anchors
|++++++++++ | 19% ~02m 34s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 651 anchors
|+++++++++++ | 21% ~02m 33s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 594 anchors
|+++++++++++ | 22% ~02m 28s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 639 anchors
|++++++++++++ | 23% ~02m 25s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 804 anchors
|+++++++++++++ | 24% ~02m 23s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 354 anchors
|+++++++++++++ | 26% ~02m 18s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 506 anchors
|++++++++++++++ | 27% ~02m 14s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 520 anchors
|+++++++++++++++ | 28% ~02m 11s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 577 anchors
|+++++++++++++++ | 29% ~02m 07s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 514 anchors
|++++++++++++++++ | 31% ~02m 04s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 833 anchors
|+++++++++++++++++ | 32% ~02m 01s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 379 anchors
|+++++++++++++++++ | 33% ~01m 57s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 480 anchors
|++++++++++++++++++ | 35% ~01m 53s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 806 anchors
|++++++++++++++++++ | 36% ~01m 50s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 171 anchors
|+++++++++++++++++++ | 37% ~01m 47s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 205 anchors
|++++++++++++++++++++ | 38% ~01m 43s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 238 anchors
|++++++++++++++++++++ | 40% ~01m 40s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 275 anchors
|+++++++++++++++++++++ | 41% ~01m 38s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 248 anchors
|++++++++++++++++++++++ | 42% ~01m 34s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 199 anchors
|++++++++++++++++++++++ | 44% ~01m 31s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 217 anchors
|+++++++++++++++++++++++ | 45% ~01m 28s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 251 anchors
|++++++++++++++++++++++++ | 46% ~01m 25s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 593 anchors
|++++++++++++++++++++++++ | 47% ~01m 24s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 617 anchors
|+++++++++++++++++++++++++ | 49% ~01m 21s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 662 anchors
|+++++++++++++++++++++++++ | 50% ~01m 19s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 660 anchors
|++++++++++++++++++++++++++ | 51% ~01m 18s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 350 anchors
|+++++++++++++++++++++++++++ | 53% ~01m 15s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 613 anchors
|+++++++++++++++++++++++++++ | 54% ~01m 13s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 795 anchors
|++++++++++++++++++++++++++++ | 55% ~01m 11s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 581 anchors
|+++++++++++++++++++++++++++++ | 56% ~01m 08s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 227 anchors
|+++++++++++++++++++++++++++++ | 58% ~01m 05s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 499 anchors
|++++++++++++++++++++++++++++++ | 59% ~01m 04s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 505 anchors
|+++++++++++++++++++++++++++++++ | 60% ~01m 01s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 528 anchors
|+++++++++++++++++++++++++++++++ | 62% ~60s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 494 anchors
|++++++++++++++++++++++++++++++++ | 63% ~58s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 289 anchors
|+++++++++++++++++++++++++++++++++ | 64% ~55s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 538 anchors
|+++++++++++++++++++++++++++++++++ | 65% ~53s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 399 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~51s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 399 anchors
|++++++++++++++++++++++++++++++++++ | 68% ~48s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 169 anchors
|+++++++++++++++++++++++++++++++++++ | 69% ~46s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 406 anchors
|++++++++++++++++++++++++++++++++++++ | 71% ~44s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 408 anchors
|++++++++++++++++++++++++++++++++++++ | 72% ~42s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 422 anchors
|+++++++++++++++++++++++++++++++++++++ | 73% ~40s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 518 anchors
|++++++++++++++++++++++++++++++++++++++ | 74% ~38s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 542 anchors
|++++++++++++++++++++++++++++++++++++++ | 76% ~36s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 307 anchors
|+++++++++++++++++++++++++++++++++++++++ | 77% ~34s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 474 anchors
|++++++++++++++++++++++++++++++++++++++++ | 78% ~32s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 452 anchors
|++++++++++++++++++++++++++++++++++++++++ | 79% ~30s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 405 anchors
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~28s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 197 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~26s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 611 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~24s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 389 anchors
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~22s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 186 anchors
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~20s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 198 anchors
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~18s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 187 anchors
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~16s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 243 anchors
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~15s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 221 anchors
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~13s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 184 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~11s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 180 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~09s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 202 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 169 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 188 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 172 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~02s
Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 160 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 16s
integrated_seurat <- IntegrateData(anchorset = int.anchors, dims = 1:30, k.weight = 72)
Merging dataset 5 into 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 12 into 10
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 8 into 7
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 9 into 4 5
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 11 into 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 6 11 into 1
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 13 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 2 13 into 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 10 12 into 7 8
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 2 13 into 1 6 11
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 5 9 into 7 8 10 12
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 7 8 10 12 4 5 9 into 1 6 11 3 2 13
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
# the k value is the problem
# must set the k weight to the lowest cell count
Save so far
saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/seu_ann_June28.RDS")
saveRDS(integrated_seurat, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/Integr_seu_ann_June28.RDS")
DimPlot(seu, group.by = "souporcell_assignment", split.by = "CellTypes")
DimPlot(integrated_seurat, group.by = "CellTypes")
NA
NA
DimPlot(integrated_seurat, group.by = "souporcell_assignment", split.by = "CellTypes")
NA
NA
integrated_seurat <- FindNeighbors(integrated_seurat, dims = 1:20, k.param = 81)
Computing nearest neighbor graph
Computing SNN
integrated_seurat <- FindClusters(integrated_seurat, resolution = c(0.6,1) )
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2251770
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8726
Number of communities: 13
Elapsed time: 8 seconds
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 14643
Number of edges: 2251770
Running Louvain algorithm...
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.8358
Number of communities: 17
Elapsed time: 10 seconds
DimPlot(integrated_seurat, group.by = "orig.ident")
#much better integration
#integrated_seurat$integrated_snn_res.0.6
DimPlot(integrated_seurat, group.by = "integrated_snn_res.0.6", label=TRUE)
DimPlot(integrated_seurat, group.by = "integrated_snn_res.1", label = TRUE)
Now cells per cluster
table(integrated_seurat$integrated_snn_res.0.6, integrated_seurat$orig.ident)
0 1 10 11 12 2 3 4 5 6 7 8 9
0 94 215 67 587 474 38 232 12 15 41 185 125 356
1 187 114 67 511 290 7 414 6 18 33 178 53 200
2 161 290 41 5 553 28 46 31 83 150 52 105 287
3 140 111 52 417 302 16 281 11 31 34 138 75 145
4 202 19 48 640 63 21 241 16 23 17 246 29 16
5 72 82 39 315 201 1 240 1 12 24 100 36 126
6 77 64 12 295 151 17 309 5 10 16 81 64 127
7 72 44 29 134 56 58 86 76 54 20 52 26 43
8 13 39 22 40 106 6 56 11 26 22 38 26 43
9 59 11 25 35 25 0 143 0 5 11 64 4 9
10 33 22 15 101 62 7 26 2 10 8 43 22 30
11 1 0 0 3 64 0 187 5 4 0 0 0 1
12 7 3 0 5 84 2 8 8 14 0 2 19 94
table(integrated_seurat$integrated_snn_res.1, integrated_seurat$orig.ident)
0 1 10 11 12 2 3 4 5 6 7 8 9
0 186 112 65 506 286 6 413 5 17 31 175 51 193
1 141 110 52 417 300 16 276 10 30 34 138 75 145
2 91 74 60 579 244 31 216 5 8 19 180 92 111
3 73 80 40 316 201 1 237 1 12 24 98 36 127
4 77 64 12 295 151 18 303 4 9 15 81 63 127
5 138 218 23 1 125 9 28 17 23 66 48 81 214
6 80 6 22 307 37 11 129 13 13 11 148 17 6
7 122 13 26 336 26 9 116 3 10 6 99 13 10
8 73 46 29 135 58 63 86 80 54 20 52 27 50
9 1 146 4 4 241 2 8 4 7 26 5 31 248
10 9 25 16 0 344 16 18 9 45 71 2 12 29
11 14 43 23 40 108 6 57 11 28 23 39 26 45
12 59 11 26 36 25 0 146 0 5 11 65 4 9
13 33 22 15 103 62 7 26 2 10 8 43 22 31
14 1 0 0 3 64 1 198 5 4 0 0 0 1
15 7 3 0 5 85 2 8 8 14 0 2 20 95
16 13 41 4 5 74 3 4 7 16 11 4 14 36
Which cells match to which subject?
From Michaels correlation:
code.tb <- read.table(file = "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/Genotype_ID_key.txt",
header = TRUE, sep = "\t")
class(code.tb)
[1] "data.frame"
seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/IntegratedSeuratGraphsJune28.RDS")
Warning in gzfile(file, "rb") :
cannot open compressed file '/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/IntegratedSeuratGraphsJune28.RDS', probable reason 'No such file or directory'
Error in gzfile(file, "rb") : cannot open the connection
# add the assigned lines into the seurat object
# Assuming your data frame is named 'lookup_df' and has columns 'Genotype_ID' and 'Cluster_ID'
lookup_table <- code.tb[, c("Cluster_ID", "Genotype_ID")]
# add the a new metadata slot
seu$Genotype_ID <- lookup_table$Genotype_ID[match(seu$souporcell_assignment, lookup_table$Cluster_ID)]
seu$Genotype_ID <- ifelse(
seu$souporcell_assignment %in% lookup_table$Cluster_ID,
lookup_table$Genotype_ID[match(seu$souporcell_assignment, lookup_table$Cluster_ID)],
"unassigned"
)
unique(seu$Genotype_ID)
[1] "28_CH79DIVUZHD100462" "22_CH47DIVUZHD100456"
[3] "4_CH96GSAUZHD100576" "19_CH31DIVUZHD100453"
[5] "unassigned" "29_CH52DIVUZHD100463"
[7] "saliva_mr-023__saliva_mr-023_" "23_CH20DIVUZHD100457"
[9] "32_CH97GSAUZHD100946" "3_CH26GSAUZHD100575"
Add genotype to intergrated object
lookup_table <- code.tb[, c("Cluster_ID", "Genotype_ID")]
# add the a new metadata slot
integrated_seurat$Genotype_ID <- lookup_table$Genotype_ID[match(integrated_seurat$souporcell_assignment, lookup_table$Cluster_ID)]
integrated_seurat$Genotype_ID <- ifelse(
integrated_seurat$souporcell_assignment %in% lookup_table$Cluster_ID,
lookup_table$Genotype_ID[match(integrated_seurat$souporcell_assignment, lookup_table$Cluster_ID)],
"unassigned"
)
unique(integrated_seurat$Genotype_ID)
[1] "28_CH79DIVUZHD100462" "22_CH47DIVUZHD100456"
[3] "4_CH96GSAUZHD100576" "19_CH31DIVUZHD100453"
[5] "unassigned" "29_CH52DIVUZHD100463"
[7] "saliva_mr-023__saliva_mr-023_" "23_CH20DIVUZHD100457"
[9] "32_CH97GSAUZHD100946" "3_CH26GSAUZHD100575"
Integrated object annotations
table(integrated_seurat$CellTypes, integrated_seurat$integrated_snn_res.0.6)
0 1 2 3 4 5 6 7 8 9 10
glia 650 259 1217 319 21 163 140 38 119 18 16
glia-APOE 265 122 573 133 23 84 38 10 124 11 4
Neuron 2 3 0 1 1405 0 0 2 0 0 0
NPC 1043 1050 13 818 66 594 480 12 192 302 7
NPC-mixed 54 1 1 6 20 1 26 674 2 1 340
NPC-pluri 279 455 13 337 26 279 379 10 3 56 9
NPC-TNC 148 188 15 139 20 128 165 4 8 3 5
11 12
glia 1 3
glia-APOE 4 3
Neuron 0 1
NPC 3 4
NPC-mixed 0 16
NPC-pluri 257 7
NPC-TNC 0 212
Idents(integrated_seurat) <- "integrated_snn_res.0.6"
ClusterMarkers <- FindAllMarkers(integrated_seurat, only.pos = TRUE)
Calculating cluster 0
| | 0 % ~calculating
|+++ | 5 % ~01s
|+++++ | 10% ~01s
|++++++++ | 15% ~01s
|++++++++++ | 20% ~01s
|+++++++++++++ | 25% ~01s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++++ | 35% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++++ | 45% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++++ | 55% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++++ | 65% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~00s
|++++++++++++++++++++++++++++++++++++++ | 75% ~00s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01s
Calculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~10s
|++ | 3 % ~10s
|+++ | 4 % ~10s
|+++ | 6 % ~10s
|++++ | 7 % ~10s
|+++++ | 9 % ~10s
|++++++ | 10% ~10s
|++++++ | 12% ~10s
|+++++++ | 13% ~10s
|++++++++ | 14% ~10s
|++++++++ | 16% ~09s
|+++++++++ | 17% ~09s
|++++++++++ | 19% ~09s
|+++++++++++ | 20% ~09s
|+++++++++++ | 22% ~09s
|++++++++++++ | 23% ~09s
|+++++++++++++ | 25% ~08s
|++++++++++++++ | 26% ~08s
|++++++++++++++ | 28% ~08s
|+++++++++++++++ | 29% ~08s
|++++++++++++++++ | 30% ~08s
|++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 35% ~07s
|+++++++++++++++++++ | 36% ~07s
|+++++++++++++++++++ | 38% ~07s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 41% ~07s
|++++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~06s
|+++++++++++++++++++++++ | 45% ~06s
|++++++++++++++++++++++++ | 46% ~06s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 52% ~05s
|+++++++++++++++++++++++++++ | 54% ~05s
|++++++++++++++++++++++++++++ | 55% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|+++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 59% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~04s
|++++++++++++++++++++++++++++++++ | 62% ~04s
|++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 70% ~03s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 72% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
Calculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~24s
|++ | 2 % ~24s
|++ | 4 % ~24s
|+++ | 5 % ~23s
|++++ | 6 % ~23s
|++++ | 8 % ~23s
|+++++ | 9 % ~22s
|+++++ | 10% ~22s
|++++++ | 11% ~22s
|+++++++ | 12% ~21s
|+++++++ | 14% ~21s
|++++++++ | 15% ~21s
|+++++++++ | 16% ~21s
|+++++++++ | 18% ~20s
|++++++++++ | 19% ~20s
|++++++++++ | 20% ~20s
|+++++++++++ | 21% ~19s
|++++++++++++ | 22% ~19s
|++++++++++++ | 24% ~19s
|+++++++++++++ | 25% ~18s
|++++++++++++++ | 26% ~18s
|++++++++++++++ | 28% ~18s
|+++++++++++++++ | 29% ~18s
|+++++++++++++++ | 30% ~17s
|++++++++++++++++ | 31% ~17s
|+++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 34% ~16s
|++++++++++++++++++ | 35% ~16s
|+++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 38% ~15s
|++++++++++++++++++++ | 39% ~15s
|++++++++++++++++++++ | 40% ~15s
|+++++++++++++++++++++ | 41% ~14s
|++++++++++++++++++++++ | 42% ~14s
|++++++++++++++++++++++ | 44% ~14s
|+++++++++++++++++++++++ | 45% ~14s
|++++++++++++++++++++++++ | 46% ~13s
|++++++++++++++++++++++++ | 48% ~13s
|+++++++++++++++++++++++++ | 49% ~13s
|+++++++++++++++++++++++++ | 50% ~12s
|++++++++++++++++++++++++++ | 51% ~12s
|+++++++++++++++++++++++++++ | 52% ~12s
|+++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~11s
|+++++++++++++++++++++++++++++ | 56% ~11s
|+++++++++++++++++++++++++++++ | 58% ~11s
|++++++++++++++++++++++++++++++ | 59% ~10s
|++++++++++++++++++++++++++++++ | 60% ~10s
|+++++++++++++++++++++++++++++++ | 61% ~10s
|++++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 64% ~09s
|+++++++++++++++++++++++++++++++++ | 65% ~09s
|++++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 68% ~08s
|+++++++++++++++++++++++++++++++++++ | 69% ~08s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|+++++++++++++++++++++++++++++++++++++ | 72% ~07s
|+++++++++++++++++++++++++++++++++++++ | 74% ~07s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s
Calculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~13s
|++ | 2 % ~12s
|++ | 4 % ~12s
|+++ | 5 % ~12s
|+++ | 6 % ~12s
|++++ | 7 % ~12s
|+++++ | 8 % ~11s
|+++++ | 10% ~11s
|++++++ | 11% ~11s
|++++++ | 12% ~11s
|+++++++ | 13% ~11s
|++++++++ | 14% ~11s
|++++++++ | 15% ~10s
|+++++++++ | 17% ~10s
|+++++++++ | 18% ~10s
|++++++++++ | 19% ~10s
|+++++++++++ | 20% ~10s
|+++++++++++ | 21% ~10s
|++++++++++++ | 23% ~10s
|++++++++++++ | 24% ~09s
|+++++++++++++ | 25% ~09s
|++++++++++++++ | 26% ~09s
|++++++++++++++ | 27% ~09s
|+++++++++++++++ | 29% ~09s
|+++++++++++++++ | 30% ~09s
|++++++++++++++++ | 31% ~08s
|+++++++++++++++++ | 32% ~08s
|+++++++++++++++++ | 33% ~08s
|++++++++++++++++++ | 35% ~08s
|++++++++++++++++++ | 36% ~08s
|+++++++++++++++++++ | 37% ~08s
|++++++++++++++++++++ | 38% ~08s
|++++++++++++++++++++ | 39% ~07s
|+++++++++++++++++++++ | 40% ~07s
|+++++++++++++++++++++ | 42% ~07s
|++++++++++++++++++++++ | 43% ~07s
|+++++++++++++++++++++++ | 44% ~07s
|+++++++++++++++++++++++ | 45% ~07s
|++++++++++++++++++++++++ | 46% ~07s
|++++++++++++++++++++++++ | 48% ~06s
|+++++++++++++++++++++++++ | 49% ~06s
|+++++++++++++++++++++++++ | 50% ~06s
|++++++++++++++++++++++++++ | 51% ~06s
|+++++++++++++++++++++++++++ | 52% ~06s
|+++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|++++++++++++++++++++++++++++ | 56% ~05s
|+++++++++++++++++++++++++++++ | 57% ~05s
|++++++++++++++++++++++++++++++ | 58% ~05s
|++++++++++++++++++++++++++++++ | 60% ~05s
|+++++++++++++++++++++++++++++++ | 61% ~05s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|+++++++++++++++++++++++++++++++++ | 64% ~04s
|+++++++++++++++++++++++++++++++++ | 65% ~04s
|++++++++++++++++++++++++++++++++++ | 67% ~04s
|++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~03s
|+++++++++++++++++++++++++++++++++++++ | 73% ~03s
|+++++++++++++++++++++++++++++++++++++ | 74% ~03s
|++++++++++++++++++++++++++++++++++++++ | 75% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~02s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12s
Calculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~22s
|++ | 2 % ~21s
|++ | 3 % ~21s
|+++ | 4 % ~21s
|+++ | 5 % ~21s
|++++ | 6 % ~20s
|++++ | 7 % ~20s
|+++++ | 8 % ~20s
|+++++ | 9 % ~20s
|++++++ | 11% ~19s
|++++++ | 12% ~19s
|+++++++ | 13% ~19s
|+++++++ | 14% ~19s
|++++++++ | 15% ~18s
|++++++++ | 16% ~18s
|+++++++++ | 17% ~18s
|+++++++++ | 18% ~18s
|++++++++++ | 19% ~18s
|++++++++++ | 20% ~17s
|+++++++++++ | 21% ~17s
|++++++++++++ | 22% ~17s
|++++++++++++ | 23% ~17s
|+++++++++++++ | 24% ~16s
|+++++++++++++ | 25% ~16s
|++++++++++++++ | 26% ~16s
|++++++++++++++ | 27% ~16s
|+++++++++++++++ | 28% ~15s
|+++++++++++++++ | 29% ~15s
|++++++++++++++++ | 31% ~15s
|++++++++++++++++ | 32% ~15s
|+++++++++++++++++ | 33% ~15s
|+++++++++++++++++ | 34% ~14s
|++++++++++++++++++ | 35% ~14s
|++++++++++++++++++ | 36% ~14s
|+++++++++++++++++++ | 37% ~14s
|+++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~13s
|++++++++++++++++++++ | 40% ~13s
|+++++++++++++++++++++ | 41% ~13s
|++++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~12s
|+++++++++++++++++++++++ | 44% ~12s
|+++++++++++++++++++++++ | 45% ~12s
|++++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~11s
|+++++++++++++++++++++++++ | 48% ~11s
|+++++++++++++++++++++++++ | 49% ~11s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~10s
|+++++++++++++++++++++++++++ | 54% ~10s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~09s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|++++++++++++++++++++++++++++++++ | 62% ~08s
|++++++++++++++++++++++++++++++++ | 63% ~08s
|+++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|++++++++++++++++++++++++++++++++++ | 66% ~07s
|++++++++++++++++++++++++++++++++++ | 67% ~07s
|+++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~06s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=22s
Calculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 3 % ~05s
|++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 7 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~05s
|++++++ | 11% ~05s
|++++++ | 12% ~05s
|+++++++ | 13% ~05s
|++++++++ | 14% ~05s
|++++++++ | 16% ~05s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 26% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|++++++++++++++++ | 30% ~04s
|++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 33% ~04s
|++++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|++++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 53% ~03s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|+++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
Calculating cluster 6
| | 0 % ~calculating
|+ | 2 % ~05s
|++ | 3 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~04s
|++++++ | 11% ~04s
|+++++++ | 12% ~04s
|+++++++ | 14% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 17% ~04s
|++++++++++ | 18% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 23% ~04s
|+++++++++++++ | 24% ~04s
|+++++++++++++ | 26% ~04s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 29% ~03s
|++++++++++++++++ | 30% ~03s
|++++++++++++++++ | 32% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 35% ~03s
|+++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 47% ~03s
|+++++++++++++++++++++++++ | 48% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 53% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 71% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
Calculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 2 % ~05s
|++ | 4 % ~05s
|+++ | 5 % ~05s
|++++ | 6 % ~05s
|++++ | 8 % ~05s
|+++++ | 9 % ~05s
|+++++ | 10% ~05s
|++++++ | 11% ~05s
|+++++++ | 12% ~05s
|+++++++ | 14% ~05s
|++++++++ | 15% ~05s
|+++++++++ | 16% ~05s
|+++++++++ | 18% ~05s
|++++++++++ | 19% ~04s
|++++++++++ | 20% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 24% ~04s
|+++++++++++++ | 25% ~04s
|++++++++++++++ | 26% ~04s
|++++++++++++++ | 28% ~04s
|+++++++++++++++ | 29% ~04s
|+++++++++++++++ | 30% ~04s
|++++++++++++++++ | 31% ~04s
|+++++++++++++++++ | 32% ~04s
|+++++++++++++++++ | 34% ~04s
|++++++++++++++++++ | 35% ~04s
|+++++++++++++++++++ | 36% ~04s
|+++++++++++++++++++ | 38% ~03s
|++++++++++++++++++++ | 39% ~03s
|++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 41% ~03s
|++++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 44% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~03s
|+++++++++++++++++++++++++ | 50% ~03s
|++++++++++++++++++++++++++ | 51% ~03s
|+++++++++++++++++++++++++++ | 52% ~03s
|+++++++++++++++++++++++++++ | 54% ~03s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 56% ~02s
|+++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 59% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 62% ~02s
|++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 65% ~02s
|++++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 71% ~02s
|+++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=06s
Calculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~22s
|+ | 2 % ~22s
|++ | 3 % ~22s
|++ | 4 % ~22s
|+++ | 5 % ~22s
|+++ | 6 % ~21s
|++++ | 7 % ~21s
|++++ | 8 % ~21s
|+++++ | 9 % ~21s
|+++++ | 10% ~21s
|++++++ | 11% ~20s
|++++++ | 12% ~20s
|+++++++ | 13% ~20s
|+++++++ | 14% ~20s
|++++++++ | 15% ~19s
|++++++++ | 16% ~19s
|+++++++++ | 17% ~19s
|+++++++++ | 18% ~19s
|++++++++++ | 19% ~18s
|++++++++++ | 20% ~18s
|+++++++++++ | 21% ~18s
|+++++++++++ | 22% ~18s
|++++++++++++ | 23% ~18s
|++++++++++++ | 24% ~17s
|+++++++++++++ | 25% ~17s
|+++++++++++++ | 26% ~17s
|++++++++++++++ | 27% ~17s
|++++++++++++++ | 28% ~16s
|+++++++++++++++ | 29% ~16s
|+++++++++++++++ | 30% ~16s
|++++++++++++++++ | 31% ~16s
|++++++++++++++++ | 32% ~16s
|+++++++++++++++++ | 33% ~15s
|+++++++++++++++++ | 34% ~15s
|++++++++++++++++++ | 35% ~15s
|++++++++++++++++++ | 36% ~15s
|+++++++++++++++++++ | 37% ~14s
|+++++++++++++++++++ | 38% ~14s
|++++++++++++++++++++ | 39% ~14s
|++++++++++++++++++++ | 40% ~14s
|+++++++++++++++++++++ | 41% ~14s
|+++++++++++++++++++++ | 42% ~13s
|++++++++++++++++++++++ | 43% ~13s
|++++++++++++++++++++++ | 44% ~13s
|+++++++++++++++++++++++ | 45% ~13s
|+++++++++++++++++++++++ | 46% ~12s
|++++++++++++++++++++++++ | 47% ~12s
|++++++++++++++++++++++++ | 48% ~12s
|+++++++++++++++++++++++++ | 49% ~12s
|+++++++++++++++++++++++++ | 50% ~12s
|++++++++++++++++++++++++++ | 51% ~11s
|++++++++++++++++++++++++++ | 52% ~11s
|+++++++++++++++++++++++++++ | 53% ~11s
|+++++++++++++++++++++++++++ | 54% ~11s
|++++++++++++++++++++++++++++ | 55% ~10s
|++++++++++++++++++++++++++++ | 56% ~10s
|+++++++++++++++++++++++++++++ | 57% ~10s
|+++++++++++++++++++++++++++++ | 58% ~10s
|++++++++++++++++++++++++++++++ | 59% ~09s
|++++++++++++++++++++++++++++++ | 60% ~09s
|+++++++++++++++++++++++++++++++ | 61% ~09s
|+++++++++++++++++++++++++++++++ | 62% ~09s
|++++++++++++++++++++++++++++++++ | 63% ~09s
|++++++++++++++++++++++++++++++++ | 64% ~08s
|+++++++++++++++++++++++++++++++++ | 65% ~08s
|+++++++++++++++++++++++++++++++++ | 66% ~08s
|++++++++++++++++++++++++++++++++++ | 67% ~08s
|++++++++++++++++++++++++++++++++++ | 68% ~07s
|+++++++++++++++++++++++++++++++++++ | 69% ~07s
|+++++++++++++++++++++++++++++++++++ | 70% ~07s
|++++++++++++++++++++++++++++++++++++ | 71% ~07s
|++++++++++++++++++++++++++++++++++++ | 72% ~06s
|+++++++++++++++++++++++++++++++++++++ | 73% ~06s
|+++++++++++++++++++++++++++++++++++++ | 74% ~06s
|++++++++++++++++++++++++++++++++++++++ | 75% ~06s
|++++++++++++++++++++++++++++++++++++++ | 76% ~06s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~05s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~05s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~05s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~04s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~04s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~04s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~03s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~03s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=23s
Calculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~05s
|++ | 3 % ~05s
|+++ | 4 % ~05s
|+++ | 6 % ~04s
|++++ | 7 % ~04s
|+++++ | 9 % ~04s
|++++++ | 10% ~04s
|++++++ | 12% ~04s
|+++++++ | 13% ~04s
|++++++++ | 15% ~04s
|+++++++++ | 16% ~04s
|+++++++++ | 18% ~04s
|++++++++++ | 19% ~04s
|+++++++++++ | 21% ~04s
|++++++++++++ | 22% ~04s
|++++++++++++ | 24% ~03s
|+++++++++++++ | 25% ~03s
|++++++++++++++ | 27% ~03s
|+++++++++++++++ | 28% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++ | 31% ~03s
|+++++++++++++++++ | 33% ~03s
|++++++++++++++++++ | 34% ~03s
|++++++++++++++++++ | 36% ~03s
|+++++++++++++++++++ | 37% ~03s
|++++++++++++++++++++ | 39% ~03s
|+++++++++++++++++++++ | 40% ~03s
|+++++++++++++++++++++ | 42% ~03s
|++++++++++++++++++++++ | 43% ~03s
|+++++++++++++++++++++++ | 45% ~03s
|++++++++++++++++++++++++ | 46% ~03s
|++++++++++++++++++++++++ | 48% ~03s
|+++++++++++++++++++++++++ | 49% ~02s
|++++++++++++++++++++++++++ | 51% ~02s
|+++++++++++++++++++++++++++ | 52% ~02s
|+++++++++++++++++++++++++++ | 54% ~02s
|++++++++++++++++++++++++++++ | 55% ~02s
|+++++++++++++++++++++++++++++ | 57% ~02s
|++++++++++++++++++++++++++++++ | 58% ~02s
|++++++++++++++++++++++++++++++ | 60% ~02s
|+++++++++++++++++++++++++++++++ | 61% ~02s
|++++++++++++++++++++++++++++++++ | 63% ~02s
|+++++++++++++++++++++++++++++++++ | 64% ~02s
|+++++++++++++++++++++++++++++++++ | 66% ~02s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|+++++++++++++++++++++++++++++++++++ | 69% ~02s
|++++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++ | 72% ~01s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~01s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05s
Calculating cluster 10
| | 0 % ~calculating
|+ | 2 % ~07s
|++ | 4 % ~07s
|+++ | 6 % ~07s
|++++ | 8 % ~07s
|+++++ | 9 % ~07s
|++++++ | 11% ~07s
|+++++++ | 13% ~06s
|++++++++ | 15% ~06s
|+++++++++ | 17% ~06s
|++++++++++ | 19% ~06s
|+++++++++++ | 21% ~06s
|++++++++++++ | 23% ~06s
|+++++++++++++ | 25% ~06s
|++++++++++++++ | 26% ~05s
|+++++++++++++++ | 28% ~05s
|++++++++++++++++ | 30% ~05s
|+++++++++++++++++ | 32% ~05s
|+++++++++++++++++ | 34% ~05s
|++++++++++++++++++ | 36% ~05s
|+++++++++++++++++++ | 38% ~05s
|++++++++++++++++++++ | 40% ~04s
|+++++++++++++++++++++ | 42% ~04s
|++++++++++++++++++++++ | 43% ~04s
|+++++++++++++++++++++++ | 45% ~04s
|++++++++++++++++++++++++ | 47% ~04s
|+++++++++++++++++++++++++ | 49% ~04s
|++++++++++++++++++++++++++ | 51% ~04s
|+++++++++++++++++++++++++++ | 53% ~04s
|++++++++++++++++++++++++++++ | 55% ~03s
|+++++++++++++++++++++++++++++ | 57% ~03s
|++++++++++++++++++++++++++++++ | 58% ~03s
|+++++++++++++++++++++++++++++++ | 60% ~03s
|++++++++++++++++++++++++++++++++ | 62% ~03s
|+++++++++++++++++++++++++++++++++ | 64% ~03s
|++++++++++++++++++++++++++++++++++ | 66% ~03s
|++++++++++++++++++++++++++++++++++ | 68% ~02s
|+++++++++++++++++++++++++++++++++++ | 70% ~02s
|++++++++++++++++++++++++++++++++++++ | 72% ~02s
|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~02s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~01s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~01s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 11
| | 0 % ~calculating
|+ | 2 % ~08s
|++ | 4 % ~08s
|+++ | 5 % ~08s
|++++ | 7 % ~07s
|+++++ | 9 % ~07s
|++++++ | 11% ~07s
|+++++++ | 12% ~07s
|++++++++ | 14% ~07s
|++++++++ | 16% ~07s
|+++++++++ | 18% ~06s
|++++++++++ | 19% ~06s
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|++++++++++++++++++++++++++++++++++ | 67% ~03s
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|+++++++++++++++++++++++++++++++++++++ | 74% ~02s
|++++++++++++++++++++++++++++++++++++++ | 75% ~02s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~02s
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|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
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|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=08s
Calculating cluster 12
| | 0 % ~calculating
|+ | 1 % ~11s
|++ | 3 % ~11s
|++ | 4 % ~10s
|+++ | 5 % ~10s
|++++ | 6 % ~10s
|++++ | 8 % ~10s
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|++++++ | 10% ~10s
|++++++ | 11% ~10s
|+++++++ | 13% ~09s
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|++++++++ | 15% ~09s
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|++++++++++++++++++ | 34% ~07s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=11s
checkCelltypes(cluster_num = 12)
Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
Add annotation
# we need to set the identity to rename
Idents(integrated_seurat) <- "integrated_snn_res.0.6"
# we need to make a vector of the cell type in the same order - in the cluster order
cell_types <- c("RG", "NPC-stem","glia-stem",
"NPC-div1","Neuron","NPC-stem2","NPC-div2",
"NPC-mixed","glia","astro","NPC-div3",
"NPC-pluri","glia-RG")
names(cell_types) <- levels(integrated_seurat)
integrated_seurat <- RenameIdents(integrated_seurat, cell_types)
integrated_seurat <- AddMetaData(object=integrated_seurat, metadata=Idents(integrated_seurat), col.name = "CellTypesInt")
DimPlot(integrated_seurat, label = TRUE, label.size = 2)
Idents(integrated_seurat) <- "integrated_snn_res.0.6"
# we need to make a vector of the cell type in the same order - in the cluster order
cell_types <- c("RG", "NPC-stem","glia",
"NPC-div","Neuron","NPC-stem","NPC-div",
"NPC-mixed","glia","astro","NPC-mixed",
"NPC-stem","glia2")
names(cell_types) <- levels(integrated_seurat)
integrated_seurat <- RenameIdents(integrated_seurat, cell_types)
integrated_seurat <- AddMetaData(object=integrated_seurat, metadata=Idents(integrated_seurat), col.name = "CellTypesInt2")
DimPlot(integrated_seurat, label = TRUE, label.size = 2)
Now make a table of the assigned cell counts
seu <- integrated_seurat
gene.count <- as.data.frame(table(seu$Genotype_ID))
group.gene <- as.data.frame(table(seu$souporcell_assignment, seu$Genotype_ID))
soup.count <- as.data.frame(table(seu$souporcell_assignment))
# Assuming your long table is named 'long_table'
wide_table <- group.gene %>%
pivot_wider(names_from = Var1, values_from = Freq)
wide_table
write.csv(wide_table,"/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/SoupoGeneIDJune28.csv")
Read in annotated object saved June 28
seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/Integr_seu_ann_June28.RDS")
Check if it’s correct
DimPlot(seu)
Add meta data - diagnosis
#seu$souporcell_assignment
#seu$Genotype_ID
Idents(seu) <- "Genotype_ID"
levels(seu)
[1] "28_CH79DIVUZHD100462" "22_CH47DIVUZHD100456"
[3] "4_CH96GSAUZHD100576" "19_CH31DIVUZHD100453"
[5] "unassigned" "29_CH52DIVUZHD100463"
[7] "saliva_mr-023__saliva_mr-023_" "23_CH20DIVUZHD100457"
[9] "32_CH97GSAUZHD100946" "3_CH26GSAUZHD100575"
diagnose <- c("ADHD","Control","Control","Control",
"Unassigned","ADHD","ADHD","ADHD","ADHD-non","Control")
names(diagnose) <- levels(seu)
seu <- RenameIdents(seu, diagnose)
seu <- AddMetaData(object=seu, metadata=Idents(seu), col.name = "Diagnosis")
DimPlot(seu, label = FALSE)
Differential gene expression by cell type
colnames(seu@meta.data)
[1] "orig.ident" "nCount_RNA"
[3] "nFeature_RNA" "souporcell_status"
[5] "souporcell_assignment" "percent.MT"
[7] "RNA_snn_res.0" "RNA_snn_res.0.05"
[9] "RNA_snn_res.0.25" "RNA_snn_res.0.6"
[11] "RNA_snn_res.1" "RNA_snn_res.1.5"
[13] "seurat_clusters" "CellTypes"
[15] "CellTypes1" "Celltypes1"
[17] "integrated_snn_res.0.6" "integrated_snn_res.1"
[19] "Genotype_ID" "CellTypesInt"
[21] "CellTypesInt2" "Diagnosis"
# subset the cell type we are interested
Idents(seu) <- "CellTypesInt2"
levels(seu)
[1] "RG" "NPC-stem" "glia" "NPC-div" "Neuron" "NPC-mixed"
[7] "astro" "glia2"
# get just the NPC-div cells
NPC_div <- subset(seu, idents = "NPC-div")
levels(NPC_div)
[1] "NPC-div"
Idents(NPC_div) <- "Diagnosis"
NPC_div_DGE <- FindMarkers(NPC_div, ident.1 = "ADHD",
ident.2 = "Control", logfc.threshold = 0.01)
| | 0 % ~calculating
|+ | 1 % ~25s
|++ | 2 % ~24s
|++ | 3 % ~24s
|+++ | 4 % ~24s
|+++ | 5 % ~23s
|++++ | 6 % ~23s
|++++ | 7 % ~23s
|+++++ | 8 % ~23s
|+++++ | 9 % ~22s
|++++++ | 10% ~22s
|++++++ | 11% ~22s
|+++++++ | 12% ~22s
|+++++++ | 13% ~21s
|++++++++ | 14% ~21s
|++++++++ | 15% ~21s
|+++++++++ | 16% ~21s
|+++++++++ | 17% ~20s
|++++++++++ | 18% ~20s
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|+++++++++++ | 20% ~20s
|+++++++++++ | 21% ~19s
|++++++++++++ | 22% ~19s
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|+++++++++++++ | 24% ~19s
|+++++++++++++ | 26% ~18s
|++++++++++++++ | 27% ~18s
|++++++++++++++ | 28% ~18s
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|+++++++++++++++ | 30% ~17s
|++++++++++++++++ | 31% ~17s
|++++++++++++++++ | 32% ~17s
|+++++++++++++++++ | 33% ~17s
|+++++++++++++++++ | 34% ~16s
|++++++++++++++++++ | 35% ~16s
|++++++++++++++++++ | 36% ~16s
|+++++++++++++++++++ | 37% ~16s
|+++++++++++++++++++ | 38% ~15s
|++++++++++++++++++++ | 39% ~15s
|++++++++++++++++++++ | 40% ~15s
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|+++++++++++++++++++++ | 42% ~14s
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|+++++++++++++++++++++++ | 46% ~13s
|++++++++++++++++++++++++ | 47% ~13s
|++++++++++++++++++++++++ | 48% ~13s
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|+++++++++++++++++++++++++ | 50% ~12s
|++++++++++++++++++++++++++ | 51% ~12s
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|++++++++++++++++++++++++++++ | 54% ~11s
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|++++++++++++++++++++++++++++++ | 58% ~10s
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|++++++++++++++++++++++++++++++++ | 62% ~09s
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|++++++++++++++++++++++++++++++++++++ | 70% ~07s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=25s
Check some expression
DoHeatmap(NPC_div, group.by = "Diagnosis", features = c("XIST","KRT18","CRABP1",
"FRZB","A2M","CHCHD2"))
DotPlot(NPC_div, group.by = "Diagnosis", features = c("XIST","KRT18","CRABP1",
"FRZB","A2M","CHCHD2"))+RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
NPC-stem
Idents(seu) <- "CellTypesInt2"
# get just the NPC-stem cells
NPC_stem <- subset(seu, idents = "NPC-stem")
levels(NPC_stem)
[1] "NPC-stem"
# DGE
Idents(NPC_stem) <- "Diagnosis"
NPC_stem_DGE <- FindMarkers(NPC_stem, ident.1 = "ADHD",
ident.2 = "Control", logfc.threshold = 0.01)
| | 0 % ~calculating
|+ | 1 % ~31s
|+ | 2 % ~31s
|++ | 3 % ~31s
|++ | 4 % ~30s
|+++ | 5 % ~30s
|+++ | 6 % ~29s
|++++ | 7 % ~29s
|++++ | 8 % ~29s
|+++++ | 9 % ~28s
|+++++ | 10% ~28s
|++++++ | 11% ~28s
|++++++ | 12% ~27s
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|++++++++ | 15% ~27s
|++++++++ | 16% ~26s
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|++++++++++ | 19% ~25s
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|++++++++++++++++++++++++++ | 52% ~15s
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|+++++++++++++++++++++++++++ | 54% ~15s
|++++++++++++++++++++++++++++ | 55% ~14s
|++++++++++++++++++++++++++++ | 56% ~14s
|+++++++++++++++++++++++++++++ | 57% ~14s
|+++++++++++++++++++++++++++++ | 58% ~13s
|++++++++++++++++++++++++++++++ | 59% ~13s
|++++++++++++++++++++++++++++++ | 60% ~13s
|+++++++++++++++++++++++++++++++ | 61% ~12s
|+++++++++++++++++++++++++++++++ | 62% ~12s
|++++++++++++++++++++++++++++++++ | 63% ~12s
|++++++++++++++++++++++++++++++++ | 64% ~11s
|+++++++++++++++++++++++++++++++++ | 65% ~11s
|+++++++++++++++++++++++++++++++++ | 66% ~11s
|++++++++++++++++++++++++++++++++++ | 67% ~10s
|++++++++++++++++++++++++++++++++++ | 68% ~10s
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|+++++++++++++++++++++++++++++++++++ | 70% ~09s
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|+++++++++++++++++++++++++++++++++++++ | 73% ~08s
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|++++++++++++++++++++++++++++++++++++++ | 75% ~08s
|++++++++++++++++++++++++++++++++++++++ | 76% ~08s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~07s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~07s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~06s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~06s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~05s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~05s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~04s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=31s
write.csv(NPC_stem_DGE, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/NPC_stem_DGE.csv")
dge <- c("CA4","STMN2","KRT18","HIST1H1A","S100A10","IFI27","CLSPN","IFI6","HELLS", "ACTC1")
DoHeatmap(NPC_div, group.by = "Diagnosis", features = dge)
DotPlot(NPC_div, group.by = "Diagnosis", features = dge)+RotatedAxis()
Warning: Scaling data with a low number of groups may produce misleading results
Idents(seu) <- "CellTypesInt2"
# get just the neuron cells
neurons <- subset(seu, idents = "Neuron")
# DGE
Idents(neurons) <- "Diagnosis"
neurons_DGE <- FindMarkers(neurons, ident.1 = "ADHD",
ident.2 = "Control", logfc.threshold = 0.01)
| | 0 % ~calculating
|+ | 1 % ~15s
|++ | 2 % ~14s
|++ | 3 % ~14s
|+++ | 4 % ~14s
|+++ | 5 % ~13s
|++++ | 6 % ~13s
|++++ | 7 % ~13s
|+++++ | 9 % ~13s
|+++++ | 10% ~13s
|++++++ | 11% ~13s
|++++++ | 12% ~13s
|+++++++ | 13% ~12s
|+++++++ | 14% ~12s
|++++++++ | 15% ~12s
|++++++++ | 16% ~12s
|+++++++++ | 17% ~12s
|++++++++++ | 18% ~12s
|++++++++++ | 19% ~11s
|+++++++++++ | 20% ~11s
|+++++++++++ | 21% ~11s
|++++++++++++ | 22% ~11s
|++++++++++++ | 23% ~11s
|+++++++++++++ | 24% ~11s
|+++++++++++++ | 26% ~11s
|++++++++++++++ | 27% ~10s
|++++++++++++++ | 28% ~10s
|+++++++++++++++ | 29% ~10s
|+++++++++++++++ | 30% ~10s
|++++++++++++++++ | 31% ~10s
|++++++++++++++++ | 32% ~10s
|+++++++++++++++++ | 33% ~09s
|++++++++++++++++++ | 34% ~09s
|++++++++++++++++++ | 35% ~09s
|+++++++++++++++++++ | 36% ~09s
|+++++++++++++++++++ | 37% ~09s
|++++++++++++++++++++ | 38% ~09s
|++++++++++++++++++++ | 39% ~09s
|+++++++++++++++++++++ | 40% ~08s
|+++++++++++++++++++++ | 41% ~08s
|++++++++++++++++++++++ | 43% ~08s
|++++++++++++++++++++++ | 44% ~08s
|+++++++++++++++++++++++ | 45% ~08s
|+++++++++++++++++++++++ | 46% ~08s
|++++++++++++++++++++++++ | 47% ~07s
|++++++++++++++++++++++++ | 48% ~07s
|+++++++++++++++++++++++++ | 49% ~07s
|+++++++++++++++++++++++++ | 50% ~07s
|++++++++++++++++++++++++++ | 51% ~07s
|+++++++++++++++++++++++++++ | 52% ~07s
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|++++++++++++++++++++++++++++ | 54% ~06s
|++++++++++++++++++++++++++++ | 55% ~06s
|+++++++++++++++++++++++++++++ | 56% ~06s
|+++++++++++++++++++++++++++++ | 57% ~06s
|++++++++++++++++++++++++++++++ | 59% ~06s
|++++++++++++++++++++++++++++++ | 60% ~06s
|+++++++++++++++++++++++++++++++ | 61% ~06s
|+++++++++++++++++++++++++++++++ | 62% ~05s
|++++++++++++++++++++++++++++++++ | 63% ~05s
|++++++++++++++++++++++++++++++++ | 64% ~05s
|+++++++++++++++++++++++++++++++++ | 65% ~05s
|+++++++++++++++++++++++++++++++++ | 66% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~05s
|+++++++++++++++++++++++++++++++++++ | 68% ~04s
|+++++++++++++++++++++++++++++++++++ | 69% ~04s
|++++++++++++++++++++++++++++++++++++ | 70% ~04s
|++++++++++++++++++++++++++++++++++++ | 71% ~04s
|+++++++++++++++++++++++++++++++++++++ | 72% ~04s
|+++++++++++++++++++++++++++++++++++++ | 73% ~04s
|++++++++++++++++++++++++++++++++++++++ | 74% ~04s
|++++++++++++++++++++++++++++++++++++++ | 76% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~03s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~03s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~03s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~03s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~02s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~02s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~02s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=14s
write.csv(neurons_DGE, "/Users/rhalenathomas/Documents/Data/scRNAseq/ADHD_ZNZ_Mcgill/Neurons_DGE.csv")